EFRI BRAID: Brain-inspired Algorithms for Autonomous Robots (BAAR)

EFRI BRAID：自主机器人的类脑算法 (BAAR)

• 批准号: 2318065
• 负责人: Junmin Wang
• 金额: $ 200万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318065&HistoricalAwards=false
• 关键词: EFRI; BRAID; Brain; inspired; Algorithms

Autonomous robots, such as self-driving vehicles (SDVs) and household collaborative robots (Cobots), possess great potential to benefit society and meet several important national needs. Although artificial intelligence (AI) has made substantial progress, the current data/computational efficiency and adaptability of autonomous robots pale in comparison to humans in performing routine sensorimotor tasks such as driving and cooking. Enabling such autonomous robots to continually learn from experience and persistently improve their efficiency and resilience in the real world as humans do is imperative for their widespread deployments. This project aims to develop novel computational algorithms for robot autonomy with principles and insights of neurobiological learning and brain intelligence. The outcomes could make a multifaceted and transformative impact on autonomous robots such as SDVs, Cobots, and other intelligent robotic systems in manufacturing and healthcare applications that face the same challenges of computational/data inefficiency and adaptation inflexibility.The project seeks to provide a paradigm shift in autonomous robotic systems by incorporating brain-inspired intelligence throughout their fundamental and core capabilities of perception, planning, and continual learning. Using convergent engineering-science approaches, the project aims to create a fundamental and innovative framework of brain-inspired perception, learning, and planning algorithms for autonomous robots. The framework will be applied to SDVs and Cobots as two representative and complementary engineering systems through combined theoretical and empirical studies. Integrating brain-inspired innovations, the work will adapt and engineer the general brain-inspired methods and algorithms to SDVs and Cobots for experimental validation of the effectiveness in data- and energy-efficiency, adaptability, and resiliency. It is expected that the findings will not only provide a significant leap to SDVs and Cobots toward their real-world deployments, but also have a transformative impact on other intelligent robotic systems such as those in manufacturing and healthcare domains by improving their data/computation efficiency, adaptation resiliency, and intelligence interpretability.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

自动驾驶汽车（SDV）和家庭协作机器人（COBOTS）等自治机器人具有巨大的潜力，可以使社会受益并满足几种重要的国家需求。 尽管人工智能（AI）取得了长足的进步，但与人类在执行常规的感觉运动任务（例如驾驶和烹饪）方面相比，自主机器人的当前数据/计算效率和适应性苍白。 使这种自主机器人能够不断地从经验中学习，并像人类一样持续提高其在现实世界中的效率和韧性，这对于他们广泛的部署至关重要。该项目旨在开发新型的计算算法，以使用神经生物学学习和脑智力的原理和见解来开发机器人自主权。 结果可能会对自主机器人（例如SDV，协同机器人和其他智能机器人系统）在制造和医疗保健应用中的其他智能机器人系统产生多方面和变革性的影响，这些机器人系统面临着相同的计算/数据效率低下和适应性的挑战。持续学习。该项目使用融合工程科学方法，旨在为自动机器人的脑启发，学习和计划算法创建一个基本和创新的框架。该框架将通过合并的理论和经验研究将SDV和COBOTS应用于两个代表性和互补的工程系统。这项工作整合了大脑启发的创新，将适应和设计一般的脑启发方法和算法，以实现SDV和配备，以实验数据和能源效率，适应性和弹性的有效性。 可以预期，这些发现不仅将为SDV和配备朝着现实世界的部署提供重大飞跃，而且还会对其他智能机器人系统（例如制造业和医疗保健领域中的其他智能机器人系统）产生变革性的影响，通过提高数据/计算效率，适应能力和智力的解释能够提高其智力奖励。更广泛的影响审查标准。